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cs.AR2026
MemExplorer: Navigating the Heterogeneous Memory Design Space for Agentic Inference NPUs
Haoran Wu, Zeyu Cao, Yao Lai +15
Emerging agentic LLM workloads are driving rapidly growing demand on both memory capacity and bandwidth, with different phases of inference (e.g., prefill and decode) imposing dist…
cs.AR2025
Good things come in small packages: Should we build AI clusters with Lite-GPUs?
Burcu Canakci, Junyi Liu, Xingbo Wu +5
To match the blooming demand of generative AI workloads, GPU designers have so far been trying to pack more and more compute and memory into single complex and expensive packages.…
cs.AR2025
Managed-Retention Memory: A New Class of Memory for the AI Era
Sergey Legtchenko, Ioan Stefanovici, Richard Black +6
AI clusters today are one of the major uses of High Bandwidth Memory (HBM). However, HBM is suboptimal for AI workloads for several reasons. Analysis shows HBM is overprovisioned o…